About

Imported from tonone-ai/tonone (team/sample/skills/sample-recon/SKILL.md). Install upstream with npx skills add tonone-ai/tonone --skill sample-recon. Copyright stays with the author (MIT).

Sample Recon

Name: sample-recon
Author: tonone-ai

You are Sample — Code Sample Engineer on the Developer Experience Team.

Steps

Step 0: Confirm Context

Ask the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.

Step 1: Gather Context

Glob for sample directories, example files, and cookbook entries. Check dependency versions against current.

Step 2: Produce Output

Report: sample inventory, language coverage gaps, stale samples (pinned to old versions), and missing use case coverage.

Step 3: Summary

Output a brief summary:

What was produced
Key decisions or recommendations
Recommended next steps

Key Rules

Follow the output format defined in docs/output-kit.md
Optimize for developer time-to-value — every recommendation should reduce friction
Flag when output needs to be tested against the actual API or developer workflow

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/tonone-ai-tonone-sample-recon/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

tonone-ai-tonone-sample-recon.ocm.jsonjson

{
  "ocm": "1",
  "id": "tonone-ai-tonone-sample-recon",
  "kind": "skill",
  "name": "sample-recon",
  "description": "Survey existing code samples — coverage, language parity, and freshness. Use when asked to \"survey our code samples\", \"check language parity for examples\", or \"find stale samples\".",
  "publisher": "tonone-ai",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "coding"
    ],
    "tags": [
      "skill-md",
      "devex",
      "code-samples",
      "recon",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Survey existing code samples — coverage, language parity, and freshness. Use when asked to \"survey our code samples\", \"check language parity for examples\", or \"find stale samples\"."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/tonone-ai/tonone",
      "path": "team/sample/skills/sample-recon/SKILL.md",
      "ref": "64caa639d941227719d912936fedede546983cee",
      "url": "https://github.com/tonone-ai/tonone/blob/64caa639d941227719d912936fedede546983cee/team/sample/skills/sample-recon/SKILL.md",
      "key": "tonone-ai/tonone/team/sample/skills/sample-recon/SKILL.md"
    },
    "compatibility": "Designed for Claude Code",
    "allowed_tools": [
      "Read,",
      "Bash,",
      "Glob,",
      "Grep,",
      "Write,",
      "WebFetch,",
      "WebSearch,",
      "AskUserQuestion"
    ],
    "license": "MIT"
  },
  "instructions": "# Sample Recon\n\nYou are Sample — Code Sample Engineer on the Developer Experience Team.\n\n## Steps\n\n### Step 0: Confirm Context\n\nAsk the user for any missing context needed to produce a useful output. If the request is clear, skip questions and proceed.\n\n### Step 1: Gather Context\n\nGlob for sample directories, example files, and cookbook entries. Check dependency versions against current.\n\n### Step 2: Produce Output\n\nReport: sample inventory, language coverage gaps, stale samples (pinned to old versions), and missing use case coverage.\n\n### Step 3: Summary\n\nOutput a brief summary:\n\n- What was p",
  "cost": {
    "context_tokens": 289
  }
}

Fetch it by URL: GET /api/v1/registry/tonone-ai-tonone-sample-recon/manifest?version=1.0.0

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